Multi-Modal Wireless COVID Monitoring & Infection Alerts for Concentrated Populations

多模式无线新冠肺炎监测

• 批准号: 10274232
• 负责人: MICHAEL P. SNYDER
• 金额: $ 112.17万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-21 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10274232
• 关键词: Acute; Algorithms; Architecture; COVID detection; COVID diagnostic; COVID test; COVID testing; COVID-19 pandemic; Caring; Cellular Phone; Characteristics; Client; Clinic; Clinical; Clinical Data; Communicable Diseases; Complex; Data; Data Analyses; Data Set; Detection; Development; Devices; Diagnostic Sensitivity; Diagnostic tests; Dialysis procedure; Disabled Persons; Disease; Disease Outbreaks; Disease Surveillance; Early Diagnosis; Effectiveness; Engineering; Enrollment; Ensure; Epidemiology; Event; Filtration; Frequencies; Generations; Health; Heart Rate; Individual; Infection; Informed Consent; Infrastructure; Infusion procedures; Institutional Review Boards; Lead; Logistics; Machine Learning; Medical; Methods; Minority; Modeling; Monitor; Morbidity - disease rate; Nature; Nursing Homes; Outcome; Pathologic; Patient Recruitments; Patient Self-Report; Patients; Performance; Persons; Policies; Population; Prisons; Privacy; Process; Recommendation; Recording of previous events; Rehabilitation therapy; Reporting; Residential Facilities; Resources; Risk; Schools; Secure; Security; Sensitivity and Specificity; Services; Severe Acute Respiratory Syndrome; Signal Transduction; Site; Societies; Structure; Surveillance Methods; Symptoms; System; Systems Development; Testing; Time; Viral; Weight; Wireless Technology; Work; aerosolized; base; chemotherapy; comorbidity; coronavirus disease; dashboard; data acquisition; demographics; design; detection platform; digital health; drug rehabilitation; fitbit; fitness; higher education; improved; interoperability; machine learning algorithm; meetings; mortality; multimodality; operation; pandemic disease; rehabilitation service; remote health care; response; smartphone Application; socioeconomics; stem; surveillance data; surveillance study; transmission process; trend; wearable device; wearable sensor technology

Multi-Modal Wireless COVID Monitoring & Infection Alerts for Concentrated Populations Abstract: The high aerosolized transmissibility of COVID, long asymptomatic incubation period, and highly variable presentation attributes of the COVID pandemic have proven challenging in many settings where patchwork pandemic responses have disproportionately negatively impacted vulnerable socioeconomic, minority, and disabled sub-populations. Unfortunately, these dire trends are only made more acute in settings that feature populations with limited mobility and little to no ability to self-isolate (dense concentrated populations [DCPs]), such as residential nursing homes, schools, drug rehabilitation services, prison and psychiatric facility populations, and high-frequency essential medical services, such as chemotherapy infusion clinics or dialysis units. In these DCP settings, limited diagnostic testing, prolonged indoor contact, limitations in cleaning and filtration capacities, support staff shortages, pre-existing comorbidities, and lack of effective infectious disease surveillance systems all collude to drive an increased COVID burden in DCPs. From this, it is clear that alternative detection strategies for DCPs are urgently needed to improve local capacity to monitor COVID outbreaks, mitigate their spread, and thus reduce inequitable disease and mortality burdens in these under-resourced and often overcrowded settings. In previous work, we developed a first generation detection system using heart rate data from commercially-available Fitbit Ionic wearable devices to detect the onset of COVID and other infectious diseases up to 10 days before users self-reported symptom onset (overall sensitivity 67% prior to symptom onset). Here, we propose to further develop this system for the improved detection of COVID and other infectious diseases in DCPs using existing wearable fitness devices in a wireless and interoperable digital health framework that centralizes all wearable-derived data on PHD while tailoring its presentation and health event alert system to the IT capabilities and needs of each DCP setting. In this, not only will we adapt our existing infection detection algorithms for each DCP’s particular baseline characteristics, IT infrastructure, and needs, but also use incoming data to further optimize the performance of those algorithms for continuous improvement in the sensitivity, specificity, and alert lead time for COVID onset. This will quickly enable under-resourced DCP support staff to access and use world-class COVID surveillance data in identifying individual infection events, implementing isolation, cleaning, and testing policies, and minimizing transmission, thus reducing the burden of COVID in DCP settings and reducing DCP morbidity and mortality overall.

针对集中人群的多模式无线COVID监测和感染警报 摘要：COVID的高气溶胶传播性，长的无症状潜伏期， 和高度可变的列报属性已被证明具有挑战性， 在许多情况下，拼凑的大流行病应对措施 受影响的弱势社会经济群体、少数民族和残疾人群体。可惜这些 可怕的趋势只会在以流动性有限的人口为特征的环境中变得更加尖锐， 几乎没有自我隔离的能力（密集的集中人群[DCP]），如住宅区 疗养院、学校、戒毒所、监狱和精神病院的人口， 以及高频基本医疗服务，例如化疗输液诊所或透析 单位在这些DCP环境中，有限的诊断测试，长时间的室内接触， 清洁和过滤能力、支持人员短缺、既存合并症和缺乏 有效的传染病监测系统相互勾结，导致COVID负担增加 在DCP。由此可见，迫切需要DCP的替代检测策略 提高当地监测新冠疫情爆发、减缓其传播的能力，从而减少 在这些资源不足而且往往拥挤不堪的地区， 设置.在以前的工作中，我们开发了第一代使用心率数据的检测系统 从市售的Fitbit Ionic可穿戴设备检测COVID和其他疾病的发作 在使用者自我报告的症状发作前10天内的传染病（总体敏感性 67%在症状发作前）。在这里，我们建议进一步开发这一系统，以改善 使用现有的可穿戴健身设备检测DCP中的COVID和其他传染病 在一个无线和可互操作的数字健康框架中， 在博士学位上，同时根据IT功能定制其演示和健康事件警报系统， 每个DCP设置的需要。在这方面，我们不仅将调整我们现有的感染检测 每个DCP的特定基线特征、IT基础设施和需求的算法， 还使用传入数据来进一步优化这些算法的性能， 提高COVID发病的敏感性、特异性和预警时间。这将很快 使资源不足的DCP支持人员能够访问和使用世界一流的COVID监测 用于识别单个感染事件、实施隔离、清洁和检测的数据 政策，并最大限度地减少传播，从而减少DCP设置中的COVID负担， 降低DCP的发病率和死亡率。